Examples with TreeType org.knime.base.node.mine.treeensemble.model.TreeEnsembleModel.TreeType used on opensource projects

Example 1 with TreeType

use of org.knime.base.node.mine.treeensemble.model.TreeEnsembleModel.TreeType in project knime-core by knime.

the class RegressionTreeModel method createLearnAttributeRow.

public DataRow createLearnAttributeRow(final DataRow learnRow, final DataTableSpec learnSpec) {
    final TreeType type = getType();
    switch(type) {
        case Ordinary:
            return learnRow;
        case BitVector:
            DataCell c = learnRow.getCell(0);
            if (c.isMissing()) {
                return null;
            }
            BitVectorValue bv = (BitVectorValue) c;
            final long length = bv.length();
            int nrAttributes = getMetaData().getNrAttributes();
            if (length != nrAttributes) {
                // TODO indicate error message
                return null;
            }
            DataCell trueCell = new StringCell("1");
            DataCell falseCell = new StringCell("0");
            DataCell[] cells = new DataCell[nrAttributes];
            for (int i = 0; i < nrAttributes; i++) {
                cells[i] = bv.get(i) ? trueCell : falseCell;
            }
            return new DefaultRow(learnRow.getKey(), cells);
        case ByteVector:
            DataCell cell = learnRow.getCell(0);
            if (cell.isMissing()) {
                return null;
            }
            ByteVectorValue byteVector = (ByteVectorValue) cell;
            final long bvLength = byteVector.length();
            int nrAttr = getMetaData().getNrAttributes();
            if (bvLength != nrAttr) {
                return null;
            }
            DataCell[] bvCells = new DataCell[nrAttr];
            for (int i = 0; i < nrAttr; i++) {
                bvCells[i] = new IntCell(byteVector.get(i));
            }
            return new DefaultRow(learnRow.getKey(), bvCells);
        default:
            throw new IllegalStateException("Type unknown (not implemented): " + type);
    }
}

Example 2 with TreeType

use of org.knime.base.node.mine.treeensemble.model.TreeEnsembleModel.TreeType in project knime-core by knime.

the class RegressionTreeModel method load.

/**
 * Loads and returns new ensemble model, input is NOT closed afterwards.
 *
 * @param in ...
 * @param exec ...
 * @return ...
 * @throws IOException ...
 * @throws CanceledExecutionException ...
 */
public static RegressionTreeModel load(final InputStream in, final ExecutionMonitor exec) throws IOException, CanceledExecutionException {
    // wrapping the argument (zip input) stream in a buffered stream
    // reduces read operation from, e.g. 42s to 2s
    TreeModelDataInputStream input = new TreeModelDataInputStream(new BufferedInputStream(new NonClosableInputStream(in)));
    int version = input.readInt();
    if (version > 20140201) {
        throw new IOException("Tree Ensemble version " + version + " not supported");
    }
    TreeType type = TreeType.load(input);
    TreeMetaData metaData = TreeMetaData.load(input);
    boolean isRegression = metaData.isRegression();
    TreeModelRegression model;
    try {
        model = TreeModelRegression.load(input, metaData);
        if (input.readByte() != 0) {
            throw new IOException("Model not terminated by 0 byte");
        }
    } catch (IOException e) {
        throw new IOException("Can't read tree model. " + e.getMessage(), e);
    }
    // does not close the method argument stream!!
    input.close();
    return new RegressionTreeModel(metaData, model, type);
}

Example 3 with TreeType

use of org.knime.base.node.mine.treeensemble.model.TreeEnsembleModel.TreeType in project knime-core by knime.

the class RegressionTreeModel method getLearnAttributeSpec.

/**
 * Get a table spec representing the learn attributes (not the target!). For ordinary data it is just a subset of
 * the input columns, for bit vector data it's an expanded table spec with each bit represented by a StringCell
 * column ("0" or "1"), whose name is "Bit x".
 *
 * @param learnSpec The original learn spec (which is also the return value for ordinary data)
 * @return Such a learn attribute spec.
 */
public DataTableSpec getLearnAttributeSpec(final DataTableSpec learnSpec) {
    final TreeType type = getType();
    switch(type) {
        case Ordinary:
            return learnSpec;
        case BitVector:
            int nrAttributes = getMetaData().getNrAttributes();
            DataColumnSpec[] colSpecs = new DataColumnSpec[nrAttributes];
            for (int i = 0; i < nrAttributes; i++) {
                colSpecs[i] = new DataColumnSpecCreator(TreeBitColumnMetaData.getAttributeName(i), StringCell.TYPE).createSpec();
            }
            return new DataTableSpec(colSpecs);
        case ByteVector:
            int nrAttr = getMetaData().getNrAttributes();
            DataColumnSpec[] bvColSpecs = new DataColumnSpec[nrAttr];
            for (int i = 0; i < nrAttr; i++) {
                bvColSpecs[i] = new DataColumnSpecCreator(TreeNumericColumnMetaData.getAttributeName(i), IntCell.TYPE).createSpec();
            }
            return new DataTableSpec(bvColSpecs);
        default:
            throw new IllegalStateException("Type unknown (not implemented): " + type);
    }
}